This chapter argues that our self-knowledge is often mediated by our affective self-knowledge. In other words, we often know about ourselves by knowing our own emotions. More precisely, what Cassam has called “substantial self-knowledge” (SSK), such as self-knowledge of one's character, one's values, or one's aptitudes, is mediated by affective forecasting, which is the process of predicting one's emotional responses to possible situations. For instance, a person comes to know that she is courageous by predicting her own emotional reactions (...) to possible risks and dangers. This hypothesis explains why attaining substantive self-knowledge tends to be difficult. Attaining substantive self-knowledge is difficult because, first, SSK is mediated by affective forecasting and, second, we tend to be wrong about predicting the intensity and duration of our own emotional reactions. As a result, we can identify what is common to central cases of SSK: such cases require knowledge about complex dispositions whose manifestations involve affective responses that one is not sufficiently familiar with. One thus needs to resort to the highly fallible method of affective forecasting. (shrink)

ArgumentIt seems theoretically convenient to construe knowledge practices in financial markets and organizations as “applied economics.” Alternatively or additionally, one might argue that practitioners draw on economic knowledge in order to systematically orient their actions towards profit-maximization; models, then, are understood as devices that make calculative rationality possible. However, empirical studies do not entirely confirm these theoretical positions: Practitioners’ actual calculations are often not “framed” by models; organizations and institutions influence the choice and adoption of models; and different professional groups (...) in financial markets have diverging attitudes towards model calculations. In order to account for this diversity, this article proposes the concept ofcultures of economic and financial expertise; the concept focuses on the patterns of knowledge practices and knowledge-related self-definitions of groups within financial organizations and markets; it also facilitates an analysis of the relations between and, more specifically, the hierarchies among different practices and identities. The article then goes on to explore the process of foreign exchange forecasting in a particular bank. The description immediately reveals that two groups are involved in this process: economists and analysts. These groups maintain quite different practices and self-descriptions in relation to models: While the economists in the bank use the models as organizational resources for consistent forecasting procedures and observe data with the help of simple model structures, analysts approach model forecasts from the perspective of critics: They see limits in the variable-centered, as opposed to a “thematic” approach and they disregard a model's imposed temporality. Nevertheless, analysts use model forecasts as anchors for developing their own “paths” and stories about possible future expectation changes in the markets. The specific division of labor between economists and analysts, and specifically the dominant role of analysts in the forecasting process, fit into a larger picture: The rise of institutional investors in the foreign exchange markets and their demand for genuine market knowledge for speculative investment projects has contributed to the rise and dominance of analysts’ culture. (shrink)

Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problem...

We review de Finetti’s two coherence criteria for determinate probabilities: coherence1defined in terms of previsions for a set of events that are undominated by the status quo – previsions immune to a sure-loss – and coherence2 defined in terms of forecasts for events undominated in Brier score by a rival forecast. We propose a criterion of IP-coherence2 based on a generalization of Brier score for IP-forecasts that uses 1-sided, lower and upper, probability forecasts. However, whereas Brier score is a strictly (...) proper scoring rule for eliciting determinate probabilities, we show that there is no real-valuedstrictly proper IP-score. Nonetheless, with respect to either of two decision rules – Γ-maximin or E-admissibility-+-Γ-maximin – we give a lexicographic strictly proper IP-scoring rule that is based on Brier score. (shrink)

Forecasting involves an underlying conceptualization of probability. It is this that gives sense to the notion of precision in number that makes us think of economic forecasting as more than simply complicated guesswork. We think of it as well-founded statement, a science and not an art of numbers. However, this understanding is at odds with the nature of social reality and the attributes of the forecaster. We should think differently about how we both anticipate and make the future (...) and what this means. Foresight is perhaps a more appropriate term. This paper addresses two issues that rarely receive attention in the field of economics. First, why is there a continued high demand for economic forecasts despite their lack of success in anticipating significant turning points in any given system? Second, what are forecasts actually assuming about the nature of a system and the future state of the world? In the paper I approach these issues indirectly. My intention is to highlight their significance by setting out a series of arguments that encapsulate the characteristics of a forecaster required to match a common understanding of what forecasting is intended to do. The structure of this paper is unusual for a contribution in the field of economics. It follows a format more commonly used in analytical philosophy when the author wants to focus on a problem and where the intention is to provoke further questioning, rather than supply ready answers. As such, it should not be read as a comprehensive account of all possible approaches to methods or philosophies of forecasting and attendant issues of probability. Three points are worth stating at the outset as a guide to what follows: Forecasting tends to forget that it is conjecture and what that really means. Its scientism overburdens how it is articulated and how it is perceived. We tend to think of forecasting as degrees of precision in prediction and of successful prediction as successful description of phenomena at some future point. There is, as the following discussion demonstrates, something basically inconsistent within the implications of this minimalism. Since the greater part of the credibility of, and authority of, economics resides in its claims to effective forecasting these points are highly relevant to the state of the discipline. Read the Open Peer Discussion on this paper ». (shrink)

This article presents a two level hierarchical forecasting model developed in a consulting project for a Brazilian magazine publishing company. The first level uses a VARMA model and considers econometric variables. The second level takes into account qualitative aspects of each publication issue, and is based on polynomial networks generated by Genetic Programming (GP).

Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm have (...) been investigated for the first time in multivariate drought studies. For this purpose, two semi-arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization, during the period of 1988–2017. MSPI was calculated in 5-time windows of the multivariate drought, including MSPI3–6, MSPI6–12, MSPI3–12, MSPI12–24, and MSPI24–48. The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows had better predictions than the smaller ones. Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously. The hybrid MLP-ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP’s accuracy by 28.5%, which is a significant improvement. According to the evaluations, the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas. (shrink)

In his new foreword to this edition, Hilary Putnam forcefully rejects these nativist claims.

Forecasting is an integral part of the planning process and analogously without forecasting we cannot even attempt to understand all the complexity of today’s world, the phenomena and processes that occur in it, the complexity of situations and systems if we do not face the need from their understanding and prediction. Analysing all aspects of forecasting is a very complex and difficult task, as it is characterized by great dynamism, complexity, and specificity. Therefore, the study of (...) class='Hi'>forecasting should primarily be based on its full understanding and differentiation from other security management functions to avoid any one-sidedness. Forecast within the framework of this paper primarily has several meanings. First, significant specifics and characteristics of the forecasting are determined. Second, in the context of forecasting, the emphasis is on forecasting and prognosing. Third, theoretical explication refers to establishing the causality of forecasting and planning in order to perceive the dialectical relationship and connection. (shrink)

Through a number of studies recently published in the psychology literature, T.D. Wilson, D.T. Gilbert, and others have demonstrated that our judgments about what our future mental states will be are contaminated by various distortions. Their studies distinguish a variety of different distortions, but they refer to them all with the generic term “affective forecasting.” The findings of their studies on normal volunteers are remarkably robust and, therefore, demonstrate that we are all vulnerable to the distortions of affective (...) class='Hi'>forecasting. a. (shrink)

This study examined how the number and types of consequences considered are related to forecasting and ethical decision making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to forecast potential outcomes and make a decision about each problem. Performance pressure was manipulated by ostensibly making rewards contingent on good problem-solving performance. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical consequences of the (...) problem and consequences for others were associated with both higher quality forecasts and more ethical decisions. In addition, the identification of a larger number of consequences was negatively associated with ethical decision making. Performance pressure did not impact forecast quality or ethicality of decisions. Theoretical and practical implications of these findings are discussed. (shrink)

In the traditional decision theories the role of forecasts is to a large extent swept under the carpet. I believe that a recognition of the connections between forecasts and decisions will be of benefit both for decision theory and for the art of forecasting.In this paper I have tried to analyse which factors, apart from the utilities of the outcomes of the decision alternatives, determine the value of a decision. I have outlined two answers to the question why a (...) decision which is made on the basis of a forecast is better than a decision which is based on a guess. Neither of these answers is universally valid. An assumption which is necessary for the first answer, i.e. Good's result, is that Bayes' rule is accepted as a correct and generally applicable decision principle. The second answer, which was given with the aid of probability intervals, departed from a more general decision principle, the maximin criterion for expected utilities, which was formulated in order to evade some of the criticism against Bayes' rule. However, the argument leading to the ansser is based on the assumption that the probability intervals associated with the states of nature represent certain knowledge. For this reason this answer is only approximatively valid.As a number of quotations in the section on “the weight of evidence” show, it is not sufficient to describe the knowledge about the states of nature by a single number, representing the (subjective) probability of the state, but something else has to be invoked which measures the amount of information on which a decision is based. Several authors have tried to characterize this mysterious quantity, which here was called the weight of evidence. However, there seems to be little agreement as to how this quantity should be measured. (shrink)

This chapter contains sections titled: Hubbert's Oil Supply Forecast Energy Forecast Methodology Energy Forecast Trend References and Further Reading.

Two studies explore the frequently reported finding that affective forecasts are too extreme. In the first study, driving test candidates forecast the emotional consequences of failing. Test failers overestimated the duration of their disappointment. Greater previous experience of this emotional event did not lead to any greater accuracy of the forecasts, suggesting that learning about one's own emotions is difficult. Failers' self-assessed chances of passing were lower a week after the test than immediately prior to the test; this difference correlated (...) with the magnitude of individual immediate disappointments, suggesting the presence of a cognitive strategy for recovering from disappointments. A second study investigated the theory that undue focus on the differences between present and future biases affective forecasts. “Defocusing” that induced low-level construals of the future reduced the extremeness of affective forecasts but a higher-level construal did not. We conclude that a focusing effect may bias affective forecasts. (shrink)

The aim of this study was to explore which group-based emotion regulation goals and strategies are offered in the group culture of Jehovah's Witnesses (JWS). Based on interviews with 29 group-active JWS in Norway, a thematic analysis was conducted in which an overall pattern of cognition taking precedence over emotions was found. Due to endtime expectations and a long-term goal of eternal life in Paradise, future emotions were prioritized. The emotion regulation strategies identified among JWS were social sharing and the (...) interconnected cognitive reappraisal. A new concept, emotional forecasting, was introduced, describing a reappraisal tactic of regulation using prospects of future emotions to regulate the here and now. It was concluded that the prospection of the future is a strong regulator of emotions of the here and now and should be included in psychological models of emotion regulation. (shrink)

_ Source: _Page Count 32 The aim of this study was to explore which group-based emotion regulation goals and strategies are offered in the group culture of Jehovah’s Witnesses. Based on interviews with 29 group-active JW s in Norway, a thematic analysis was conducted in which an overall pattern of cognition taking precedence over emotions was found. Due to end-time expectations and a long-term goal of eternal life in Paradise, future emotions were prioritized. The emotion regulation strategies identified among JW (...) s were _social sharing_ and the interconnected _cognitive reappraisal_. A new concept, _emotional forecasting_, was introduced, describing a reappraisal tactic of regulation using prospects of future emotions to regulate the here and now. It was concluded that the prospection of the future is a strong regulator of emotions of the _here and now_ and should be included in psychological models of emotion regulation. (shrink)

The big-fish-little-pond effect (BFLPE) is the tendency for students to evaluate themselves more favourably when they have high rank in a low rank school than low rank in a high rank school. Research has documented the BFLPE on experienced emotions. We conducted three studies that examined forecasts of how the BFLPE influences other people’s emotions (i.e. empathic forecasts). In Study 1, participants received performance feedback about themselves or another person and reported their own affect or anticipated the other person’s affect. (...) Results extended the BFLPE to empathic forecasting. Moreover, forecasters anticipated that the BFLPE had a stronger influence on negative emotion than it actually did. Study 2 tested whether neglect of group rank underlies the BFLPE in empathic forecasting. Empathic forecasts were strongly influenced by another person’s rank in their group, but only weakly influenced by group rank. Study 3 tested whether extremity of group ranks exacerbates the BFLPE in empathic forecasting. Empathic forecasts were especially favourable (unfavourable) when a target had very high (low) rank in a very low (high) rank group. These data support the BFLPE in empathic forecasting, but also illustrate ways in which it is both similar to and different from actual experience of the effect. (shrink)

_Purpose._ The paper attempts to investigate the constructivist approach to the social world and its implications for social forecasting. _Theoretical basis._ Social forecasting is mainly based on the idea that a human is "determined ontologically". Using the methodology of the natural sciences, most predictions and forecasts fail to encompass all the multiplicity and variability of the future. The postmodern interpretation of reality gave impetus to the development of the new approaches to it. A constructivist approach to social reality (...) began to compete with essentialism. Social constructivism asserts that reality is a set of mental constructs, that it is ultimately a text. Radical constructivism interprets reality as a specific system of meanings emphasizing the artifact aspects of our reality. An interpretation of the social actors’ behavior is based on the ways of understanding accepted in a given society/community and do not possess ontological universality. The creators of social space are also its creations. _Originality._ Within the framework of the postmodern approach to reality, the second-order forecasting, or forecasting of forecasting, is particularly relevant. That means that the observers-forecasters must be included in the forecast as a part of one-unified process. At this stage, a forecaster must realize that he/she is a part of a larger system, a part of the world he/she observes (and actually creates). The situation changes dramatically – the forecaster is forced to take responsibility for his/her own observations. This ultimately leads to the "humanization" of forecasting. Acting in our world full of uncertainty, unpredictability, and turbulence, modern researchers of the future should be mindful of powerful social constructs of reality. _Conclusions._ Social forecasting should be embedded in a wider context, which requires a joint effort of philosophers, foresight practitioners, historians, psychologists, sociologists, political scientists, religious scholars, anthropologists, etc. To develop promising visions and scenarios of the future, it is necessary to answer the question "Why?", which is the task of philosophically oriented research, because without this answer, we will deal with the consequences; and the implementation of the negative scenarios will reproduce itself in new socio-cultural and historical conditions. An in-depth understanding of this "Why?" provides opportunities to be in the flow of transformations. The study of the deep mental processes of the actors of social changes, the multidimensional influence on the transformation of social structures can gradually expand an answer to the question "Why?", that can cause positive changes and, accordingly, allow to create fruitful projects of the future and form effective behavioral strategies that correspond to the desired level of social development. (shrink)

University spin-outs, creating businesses from university intellectual property, are a relatively common phenomena. As a knowledge transfer channel, the spin-out business model is attracting extensive attention. In this paper, the impacts of six equities on the acquisition of USOs, including founders, university, banks, business angels, venture capitals, and other equity, are comprehensively analyzed based on theoretical and empirical studies. Firstly, the average distribution of spin-out equity at formation is calculated based on the sample data of 350 UK USOs. According to (...) this distribution, a radial basis function neural network model is employed to forecast the effects of each equity on the acquisition. To improve the classification accuracy, the novel set-membership method is adopted in the training process of the RBF NN. Furthermore, a simulation test is carried out to measure the effects of six equities on the acquisition of USOs. The simulation results show that the increase of university’s equity has a negative effect on the acquisition of USOs, whereas the increase of remaining five equities has positive effects. Finally, three suggestions are provided to promote the development and growth of USOs. (shrink)

Purpose of the article is to predict the interrelationship between the change of financial institutions loan portfolio and activities of the main economic sectors in Lithuania. Coherence between financial intermediation and economic growth cause a great interest of economists during the late decade. Prevailed opinion that banking sector is the reflection of economic growth and expansion and that its role - to intermediate in the saving and investing needs, reallocating funds between economic activities, was replaced by sentiment that strong and (...) stable banking sector could be not only the result of economic development, but also the cause of this growth. The purpose of the article is to accomplish forecast of the influence of financial institutions loan portfolio change to the main economic sectors in Lithuania for 2013-2017 period. Methodology/methods Analysis of variation of loan portfolio according economic sectors shows the tendencies of financing of various activities in the past. Regression - correlation analysis helps to identify the influence of loan portfolio changes and their impact for GDP and employment in separate economic sectors. The forecasting of different economic sectors was made using extrapolation methods and econometric method. Scientific aim: Economic situation in Lithuania forces to estimate decisions made by banking sector that influence not only individual countries but also the global economy. In this regard, there is a natural question of whether the process of underway stagnation should be judged as a timely economic development stage, or perhaps as the result of policies pursued by financial institutions? Prerequisites for the problem situation forces to overhaul loan portfolio managed by financial institutions, evaluate its change on the country's economy sectors, and to investigate these relationships forecasts. Findings: There is a strong link between financial sector development and economic growth. The results showed that the volume of loans has a very close interdependence with the GDP. Data obtained for loan portfolio forecasting for 2013-2017 showed that the growth of loans can be seen in all sectors except construction. Employment should grow only in public and retail sectors. Conclusions : The most relationship with GDP was established in retail and public sectors. Determining the correlation coefficients and regression equations between loans and employment the strongest relationship found in retail, industrial and construction sectors. The study encountered lack of data for 2012, since the information about the composition of the loan portfolio in different sectors since November 2011 is no longer provided to the Bank of Lithuania. (shrink)

This volume is a serious attempt to open up the subject of European philosophy of science to real thought, and provide the structural basis for the interdisciplinary development of its specialist fields, but also to provoke reflection on the idea of ‘European philosophy of science’. This efforts should foster a contemporaneous reflection on what might be meant by philosophy of science in Europe and European philosophy of science, and how in fact awareness of it could assist philosophers interpret and motivate (...) their research through a stronger collective identity. The overarching aim is to set the background for a collaborative project organising, systematising, and ultimately forging an identity for, European philosophy of science by creating research structures and developing research networks across Europe to promote its development. (shrink)

This volume is a serious attempt to open up the subject of European philosophy of science to real thought, and provide the structural basis for the interdisciplinary development of its specialist fields, but also to provoke reflection on the idea of ‘European philosophy of science’. This efforts should foster a contemporaneous reflection on what might be meant by philosophy of science in Europe and European philosophy of science, and how in fact awareness of it could assist philosophers interpret and motivate (...) their research through a stronger collective identity. The overarching aim is to set the background for a collaborative project organising, systematising, and ultimately forging an identity for, European philosophy of science by creating research structures and developing research networks across Europe to promote its development. (shrink)

Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network and long short-term memory model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is (...) that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function. (shrink)

_ Source: _Volume 39, Issue 3, pp 312 - 343 The aim of this study was to explore which group-based emotion regulation goals and strategies are offered in the group culture of Jehovah’s Witnesses. Based on interviews with 29 group-active JW s in Norway, a thematic analysis was conducted in which an overall pattern of cognition taking precedence over emotions was found. Due to end-time expectations and a long-term goal of eternal life in Paradise, future emotions were prioritized. The emotion (...) regulation strategies identified among JW s were _social sharing_ and the interconnected _cognitive reappraisal_. A new concept, _emotional forecasting_, was introduced, describing a reappraisal tactic of regulation using prospects of future emotions to regulate the here and now. It was concluded that the prospection of the future is a strong regulator of emotions of the _here and now_ and should be included in psychological models of emotion regulation. (shrink)

The article discusses the problem of the possibility of knowing the future, especially the future of social phenomena compared with the future of natural ones. This problem is formulated as a dilemma: the future can be explored or can be only constructed. The idea of constructive character of knowledge of the future is viewed in two possible interpretations.The first one is a special case of the constructivist interpretation of knowledge, according to which different pictures of the future are arbitrarily constructed (...) on the basis of information about the past and current state. The second interpretaion can be presented in the form of a praxeological question: what should be an action in relation to the future, first and foremost to the future of social phenomena - cognitive or creative. It is shown that, in the first case, there is an ability to interpret knowledge of the future from the point of view of epistemological realism, in the second case, both cognitive and creative types of activity as a pragmatic position in relation to the future are necessary and cognitive activity cannot be reduced to creative one. It is also shown that forecaster’s dilemma reflects the problematic nature of forecasting under the conditions of transformation of scientific rationality and point of view of common sense, which is observed in recent decades. (shrink)

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on first principles, and the naïve-inductivist one, based only on data. This latter view has recently gained some attention in response to the (...) availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann. (shrink)

My horoscope today says, “Focus on the small stuff.” Now, such advice does have content. It predicts that when readers interpret its words in the usual way as a guide to action, those who do what they think it recommends will, on average, feel they got more of what they wanted than those who ignored it. Even so, astrologers sure don’t make it easy for us to test their claims. If they wanted to make it easier, they would do what (...) forecasters in weather, sports, and business do: issue forecasts in a clear, standardized form. A horoscope is like a weather forecast that says, “Some rain likely nearby, soon.” It’s much easier to evaluate a forecast that a particular rain gauge has a.. (shrink)

Solar energy has become an integral part of the economy of developed countries, so it is important to monitor the pace of its development, prospects, as well as the largest companies that produce solar panels since the supply of solar energy in a particular country directly depends on them. The study analyzes the shares of Canadian Solar Inc. and First Solar Inc. The purpose of the study is to study the possibility of forecasting the stock price of solar energy (...) companies using neural networks for the purpose of subsequent investment. The recurrent neural network LSTM is used in the article and this approach is based on complexity theory. Machine learning technologies are now being actively implemented in various sectors of the economy and are considered effective. The program used assigns different significance to the data of the last months and the data for the first months of the 1st year. The first year of the last 5 years of the company’s activity is taken as the first year since more distant data no longer have significant significance for the forecast. In the course of the study, a forecast of the stock price of Canadian Solar Inc. and First Solar Inc. for 245 days was obtained. Based on the results obtained, the following conclusions were made: 20 neurons of the network is not enough to make an accurate forecast, but the level of confidence in such a forecast is high enough, neural network forecasts are applicable in investing and are accurate enough to determine medium- and long-term trends, but these forecasts are not applicable for traders. The direction of improving the accuracy of neural network predictions is promising for further research. (shrink)

This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of (...) the problem was associated with both higher quality forecasts and higher ethicality of decisions. Neither time pressure nor analytic mindset impacted forecasts or ethicality of decisions. Theoretical and practical implications of these findings are discussed. (shrink)

The deterministic economic system will also produce chaotic dynamic behaviour, so economic chaos is getting more and more attention, and the research of economic chaos forecasting methods has become an important topic at present. The traditional economic chaos forecasting models are mostly based on large samples, but in actual production activities, there are a large number of small-sample economic chaos problems, and there is still no effective solution. This paper proposes a combined forecasting model based on the (...) traditional economic chaos forecasting method. First of all, through the decision tree classification, priority selection of features, rough prediction is achieved. Secondly, we use BP neural network to make secondary prediction. Because the initial weight is randomly selected, it is easy to fall into the defect of local minimum. This paper optimizes the BP neural network. Finally, the decision tree model and the BP neural network model optimized by the improved genetic algorithm are combined, and the combined model is optimized by the improved GA. This method can take advantage of many prediction models and combine the prediction information of multiple different prediction models to effectively improve the fitting ability of the model and improve the prediction accuracy. (shrink)

The objective of this research is to develop accurate forecasting models for chlorophyll-α concentrations at various depths in El Mar Menor, Spain. Chlorophyll-α plays a crucial role in assessing eutrophication in this vulnerable ecosystem. To achieve this objective, various deep learning forecasting techniques, including long short-term memory, bidirectional long short-term memory and gated recurrent uni networks, were utilized. The models were designed to forecast the chlorophyll-α levels with a 2-week prediction horizon. To enhance the models’ accuracy, a sliding (...) window method combined with a blocked cross-validation procedure for time series was also applied to these techniques. Two input strategies were also tested in this approach: using only chlorophyll-α time series and incorporating exogenous variables. The proposed approach significantly improved the accuracy of the predictive models, no matter the forecasting technique employed. Results were remarkable, with |$\overline{\sigma}$| values reaching approximately 0.90 for the 0.5-m depth level and 0.80 for deeper levels. The proposed forecasting models and methodologies have great potential for predicting eutrophication episodes and acting as decision-making tools for environmental agencies. Accurate prediction of eutrophication episodes through these models could allow for proactive measures to be implemented, resulting in improved environmental management and the preservation of the ecosystem. (shrink)

The dynamics of the "Etang de Berre", a brackish lagoon situated close to the French Mediterranean sea coast, is strongly disturbed by freshwater inputs coming from an hydroelectric power station. The system dynamics has been described as a sequence of daily typical states from a set of physicochemical variables such as temperature, salinity and dissolved oxygen rates collected over three years by an automatic sampling station. Each daily pattern summarizes the evolution, hour by hour of the physicochemical variables. This article (...) presents results of forecasts of the states of the system subjected to the simultaneous effects of meteorological conditions and freshwater releases. We recall the main step of the classification tree method used to build up the predictive model (Classification and Regression Trees, Breiman et al., 1984) and we propose a transfer procedure in order to test the stability of the model. Results obtained on the Etang de Berre data set allow us to describe and predict the effects of the environmental variables on the system dynamics with a margin of error. The transfer procedure applied after the tree building process gives a maximum gain in prediction accuracy of about 15%. (shrink)

Water quality control helps in the estimation of water bodies and detects the span of pollutants and their effect on the neighboring environment. This is why the water quality of the northern part of Lake Manzala has been studied here from January to March, 2016. This study aims to model and create a program for linear and nonlinear regression of the water elements in Lake Manzala to assess and predict the water quality. Water samples have been extracted from various depths, (...) and physio-chemical properties and heavy metal concentrations have been evaluated. This study has proposed a new algorithm for predicting water quality called “Algorithm of Estimation Regression Model”. On the contrary, in renewable energy applications, statistical modeling and forecasting the solar radiation remains a significant issue with detect to reinforce power management. A new proposed method for forecasting the average Monthly Global Solar Energy in Queensland, Australia, is called, Converting Data Set into Markov Model. It was used to obtain Markov transition probability matrices for three and six states of the solar energy. The proposed forecasting method yielded accurate results with minimal error. (shrink)

Part 1 Background on de Finetti’s twin criteria of coherence: Coherence1: 2-sided previsions free from dominance through a Book. Coherence2: Forecasts free from dominance under Brier (squared error) score. Part 2 IP theory based on a scoring rule.

The impact of incidental emotions on decision making is well established. Incidental emotions can be differentiated on several appraisal dimensions, including certainty-uncertainty. The present research investigates the effect of certainty-uncertainty of incidental emotions on hedonic forecasting. The results of four experimental studies indicate that uncertainty associated incidental emotions, such as fear and hope, compared with certainty emotions, such as anger and happiness, amplify predicted utility. This amplification effect is confirmed for opposite utility types; uncertainty associated emotions, when compared with (...) their certainty counterparts, lead to an overprediction of positive utilities and to an underprediction of negative utilities. In line with the Appraisal-Tendency Framework (Lerner and Keltner, 2000; 2001), this effect is mediated by the prediction task uncertainty, providing evidence for a carryover process of the incidental emotion. The effect of task uncertainty on predicted utility is, in turn, partly mediated by attention to the task, suggesting that an affective adaptation process (AREA model; Wilson & Gilbert, 2008) lies behind the amplification of forecasts. Taken together, these findings extend the impact of certainty-uncertainty to the context of hedonic forecasting and further corroborate the impact of incidental emotions in judgement and decision making. (shrink)

This research examined whether youth's forecasted risk taking is best predicted by a compensatory (namely, subjective expected utility) or non-compensatory (e.g., single-factor) model. Ninety youth assessed the importance of perceived benefits, importance of perceived drawbacks, subjective probability of benefits, and subjective probability of drawbacks for 16 risky behaviors clustered evenly into recreational and health/safety domains. In both domains, there was strong support for a noncompensatory model in which only the perceived importance of the benefits of engaging in a risky behavior (...) predicted youths' forecasted engagement in risky behavior. The study overcomes earlier methodological weaknesses by fully decomposing participants' assessments into importance and probability aspects for both benefits and drawbacks. As such, the findings provide clear evidence in support of a boundedrationality perspective on youth decision making regarding risk taking. (shrink)

The Securities and Exchange Commission requires publicly held US corporations to disclose all information, whether it is positive or negative, that might be relevant to an investor's decision to buy, sell, or hold a company's securities. The decisions made by corporate managers to disclose such information can significantly affect the judgments and decisions of investors. This paper examines academic accounting research on corporate managers' voluntary disclosures of earnings forecasts and non-GAAP earnings measures. Much of the evidence from this research indicates (...) that some managers engage in opportunistic disclosure behavior that often benefits one group (managers and shareholders) at the expense of other groups (often other investors). The paper concludes by discussing the ethical implications of this behavior. (shrink)

1. The benefits resulting from a combination of quantitative (e.g. probabilistic) and qualitative (e.g. logic-based) reasoning are widely acknowledged (e.g. see Domingos et al., 2006; Poole, 2011)....

Decision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our (...) queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess. (shrink)

Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact (...) on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index as a measure of FX volatility. In particular, the three major FXVIX indices from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

Over the last few decades, the Internet has brought about a myriad of benefits to almost every aspect of our daily lives. However, malware attacks have also widely proliferated, mainly aiming at legitimate network users, resulting in millions of dollars in damages if proper protection and response measures are not settled and enforced. In this context, the paper at hand proposes MalSEIRS, a novel dynamic model, to predict malware distribution in a network based on the SEIRS epidemiological model. As a (...) result, the time-dependent rates of infection, recovery, and loss of immunity enable us to capture the complex dynamism of malware spreading behavior, which is influenced by a variety of external circumstances. In addition, we describe both offensive and defensive techniques, based on the proposed MalSEIRS model, through extensive experimentation, as well as disclosing real-life malware campaigns that can be better understood by using the suggested model. (shrink)